Autocorrelated noise appears in many solid-state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows computation fidelities presence autocorrelated classical noise. Hence...

Sufficient statistics are combinations of data in terms which the likelihood function can be rewritten without loss information. Depending on volume reduction, use sufficient as a preliminary step Bayesian analysis lead to significant increases efficiency when sampling from posterior distributions model parameters. Here we show that frequency integrand cross-correlation statistic and its varian...

A spatially autocorrelated effect exists in precipitation of a mountainous basin. This study examines the relationship between maximum annual rainfall and elevation in the Kaoping River Basin of southern Taiwan using spatial regression models (i.e. geographically weighted regression (GWR), simultaneous autoregression (SAR), and conditional autoregression (CAR)). Results show that the GWR, SAR, ...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

In this study we examine in covariance stationary time series the consequences of constructing confidence intervals for the population mean using the classical methodology based on the hypothesis of independence. As criteria we use the actual probability the confidence interval of the classical methodology to include the population mean (actual confidence level), and the ratio of the sampling e...

Applied work routinely relies on heteroskedasticity and autorcorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this paper, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference tha...

One of the major sources of uncertainty associated with geographical data in GIS arises when they are the outcome of a sampling process. It is well known that when sampling from a spatially autocorrelated homogeneous surface, stratification reduces the error variance of the estimator of the population mean. In this study, we evaluate the efficiency of different spatial sampling strategies when ...

Most commonly used control charts for monitoring quality characteristics of the processes were developed under the assumption that the observations are randomly sampled from a normal population. It is well known that these control charts have more false alarms than usual when processes are positively autocorrelated. One remedy is to adjust the control limits such that the modified control chart...